This paper develops a new approach for extremely fast detection in domains wherethe distribution of positive and negative examples is highly skewed (e.g.

Recent work has shown impressive transform-invariant modeling and clustering for sets of images of objects with similar appearance. We seek to expand these capabilities to sets of images of an object class that show considerable variation across individual instances (e.g.

This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human bodyin our examples) automatically from unlabeled data. The distinguished partof this work is that it is based on unlabeled data, i.e., the training features include both useful foreground parts and background clutter and the correspondence between the parts and detected features are unknown. We use decomposable triangulated graphs to depict the probabilistic independence of parts, but the unsupervised technique is not limited to this type of graph. In the new approach, labeling of the data (part assignments) is taken as hidden variables and the EM algorithm isapplied. A greedy algorithm is developed to select parts and to search for the optimal structure based on the differential entropy of these variables. The success of our algorithm is demonstrated by applying it to generate models of human motion automatically from unlabeled real image sequences.

A nonlinear supervised learning model, the Specialized Mappings Architecture (SMA), is described and applied to the estimation of human body pose from monocular images. The SMA consists of several specialized forward mapping functions and an inverse mapping function.Each specialized function maps certain domains of the input space (image features) onto the output space (body pose parameters). The key algorithmic problems faced are those of learning the specialized domains and mapping functions in an optimal way,as well as performing inference given inputs and knowledge of the inverse function. Solutions to these problems employ the EM algorithm and alternating choices of conditional independence assumptions.Performance of the approach is evaluated with synthetic and real video sequences of human motion. 1 Introduction In everyday life, humans can easily estimate body part locations (body pose) from relatively low-resolution images of the projected 3D world (e.g., when viewing a photograph or a video). However, body pose estimation is a very difficult computer vision problem.

We describe an algorithm for automatically learning discriminative components ofobjects with SVM classifiers. It is based on growing image parts by minimizing theoretical bounds on the error probability of an SVM. Component-based face classifiers are then combined in a second stage to yield a hierarchical SVM classifier. Experimental results in face classification show considerable robustness against rotations in depth and suggest performance at significantly better level than other face detection systems. Novel aspects of our approach are: a) an algorithm to learn component-based classification experts and their combination, b) the use of 3-D morphable models for training, and c) a maximum operation on the output of each component classifier which may be relevant for biological modelsof visual recognition.

The most common approach is to learn histograms of filter responses or, equivalently, tolearn probability distributions on features (see right panel of figure (1)). See, for example, [6], [5], [4].

There have been active research efforts in this area.

An important issue in applying SVMs to speech recognition is the ability to classify variable length sequences. This paper presents extensions to a standard scheme for handling this variable length data, the Fisher score. A more useful mapping is introduced based on the likelihood-ratio. The score-space defined by this mapping avoids some limitations of the Fisher score. Class-conditional generative modelsare directly incorporated into the definition of the score-space.

In the'missing data' approach to improving the robustness of automatic speech recognition to added noise, an initial process identifies spectraltemporal regionswhich are dominated by the speech source. The remaining regions are considered to be'missing'. In this paper we develop a connectionist approach to the problem of adapting speech recognition to the missing data case, using Recurrent Neural Networks. In contrast to methods based on Hidden Markov Models, RNNs allow us to make use of long-term time constraints and to make the problems of classification with incomplete data and imputing missing values interact. We report encouraging results on an isolated digit recognition task.

When applying unsupervised learning techniques like ICA or temporal decorrelation,a key question is whether the discovered projections arereliable. In other words: can we give error bars or can we assess the quality of our separation? We use resampling methods totackle these questions and show experimentally that our proposed variance estimations are strongly correlated to the separation error.We demonstrate that this reliability estimation can be used to choose the appropriate ICA-model, to enhance significantly theseparation performance, and, most important, to mark the components that have a actual physical meaning.